
from npu_bridge.npu_init import *
import cv2
import numpy as np
import core.utils as utils
import tensorflow as tf
from PIL import Image
return_elements = ['input/input_data:0', 'pred_sbbox/concat_2:0', 'pred_mbbox/concat_2:0', 'pred_lbbox/concat_2:0']
pb_file = './yolov3_coco.pb'
image_path = './docs/images/road.jpeg'
num_classes = 80
input_size = 416
graph = tf.Graph()
original_image = cv2.imread(image_path)
original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
original_image_size = original_image.shape[:2]
image_data = utils.image_preporcess(np.copy(original_image), [input_size, input_size])
image_data = image_data[(np.newaxis, ...)]
return_tensors = utils.read_pb_return_tensors(graph, pb_file, return_elements)
with tf.Session(graph=graph) as sess:
    (pred_sbbox, pred_mbbox, pred_lbbox) = sess.run([return_tensors[1], return_tensors[2], return_tensors[3]], feed_dict={return_tensors[0]: image_data})
pred_bbox = np.concatenate([np.reshape(pred_sbbox, ((- 1), (5 + num_classes))), np.reshape(pred_mbbox, ((- 1), (5 + num_classes))), np.reshape(pred_lbbox, ((- 1), (5 + num_classes)))], axis=0)
bboxes = utils.postprocess_boxes(pred_bbox, original_image_size, input_size, 0.3)
bboxes = utils.nms(bboxes, 0.45, method='nms')
image = utils.draw_bbox(original_image, bboxes)
image = Image.fromarray(image)
image.show()
